# Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs

arXiv: https://arxiv.org/abs/1811.12397 · HTML: https://mlsanity.com/paper/1811.12397/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs](https://mlsanity.com/paper/2203.10472/) (2022)
- [Machine Learning for Performance Prediction of Channel Bonding in Next-Generation IEEE 802.11 WLANs](https://mlsanity.com/paper/2105.14219/) (2021)
- [Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi](https://mlsanity.com/paper/2412.03076/) (2024)
- [Studying the Role of Synthetic Data for Machine Learning-based Wireless Networks Traffic Forecasting](https://mlsanity.com/paper/2601.07646/) (2026)
- [Decentralized Spatial Reuse Optimization in Wi-Fi: An Internal Regret Minimization Approach](https://mlsanity.com/paper/2602.08456/) (2026)
- [Toward Experimentation-as-a-Service in 5G/6G: The Plaza6G Prototype for AI-Assisted Trials](https://mlsanity.com/paper/2603.16356/) (2026)

## Abstract (arXiv)

Komondor is a wireless network simulator for next-generation wireless local area networks (WLANs). The simulator has been conceived as an accessible (ready-to-use) open source tool for research on wireless networks and academia. An important advantage of Komondor over other well-known wireless simulators lies in its high event processing rate, which is furnished by the simplification of the core operation. This allows outperforming the execution time of other simulators like ns-3, thus supporting large-scale scenarios with a huge number of nodes. In this paper, we provide insights into the Komondor simulator and overview its main features, development stages and use cases. The operation of Komondor is validated in a variety of scenarios against different tools: the ns-3 simulator and two analytical tools based on Continuous Time Markov Networks (CTMNs) and the Bianchi's DCF model. Results show that Komondor captures the IEEE 802.11 operation very similarly to ns-3. Finally, we discuss the potential of Komondor for simulating complex environments -- even with machine learning support -- in next-generation WLANs by easily developing new user-defined modules of code